Instructions to use gubartz/cls_scibert_abstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gubartz/cls_scibert_abstruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gubartz/cls_scibert_abstruct")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gubartz/cls_scibert_abstruct") model = AutoModelForSequenceClassification.from_pretrained("gubartz/cls_scibert_abstruct") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c79e2145beeb4ec5e03263f256f54645ec6c1f0ecc8402cfe7ecdf211159f82d
- Size of remote file:
- 440 MB
- SHA256:
- 4f028baea11c00a8e66712764031563cd41517dbb319d6ef5f0616fcaca52100
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